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Record W2331113209 · doi:10.1097/rli.0b013e3182300fe4

Dynamic Contrast-Enhanced Computed Tomography Imaging Biomarkers Correlated With Immunohistochemistry for Monitoring the Effects of Sorafenib on Experimental Prostate Carcinomas

2011· article· en· W2331113209 on OpenAlexaff
Clemens C. Cyran, Jobst C. von Einem, Philipp M. Paprottka, Bettina Schwarz, Michael Ingrisch, Olaf Dietrich, Rabea Hinkel, Christiane J. Bruns, Dirk‐André Clevert, Ralf Eschbach, Maximilian F. Reiser, Bernd J. Wintersperger, Konstantin Nikolaou

Bibliographic record

VenueInvestigative Radiology · 2011
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineVascularityProstate cancerSorafenibNuclear medicineProstateImmunohistochemistryRenal cell carcinomaUrologyPathologyCancerInternal medicineHepatocellular carcinoma

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate dynamic contrast-enhanced computed tomography (DCE-CT) for monitoring the effects of sorafenib on experimental prostate carcinomas in rats by quantitative assessments of tumor microcirculation parameters with immunohistochemical validation. MATERIAL AND METHODS: Prostate carcinoma allografts (MLLB-2) implanted subcutaneously in male Copenhagen rats (n=16) were imaged at baseline and after a 1-week treatment course of sorafenib using DCE-CT with iopromide (Ultravist 370, Bayer Pharma, Berlin, Germany) on a dual-source 128-slice CT (Somatom Definition FLASH, Siemens Healthcare, Forchheim, Germany). Scan parameters were as follows: detector width, 38.4 mm; contrast agent volume, 2 mL/kg bodyweight; injection rate, 0.5 mL/s; scan duration, 90 seconds; and temporal resolution, 0.5 seconds. The treatment group (n=8) received daily applications of sorafenib (10 mg/kg bodyweight) via gavage. Quantitative parameters of tumor microcirculation (plasma flow, mL/100 mL/min), endothelial permeability-surface area product (PS, mL/100 mL/min), and tumor vascularity (plasma volume, %) were calculated using a 2-compartment uptake model. DCE-CT parameters were correlated with immunohistochemical assessments of tumor vascularity (RECA-1), cell proliferation (Ki-67), and apoptosis (TUNEL). RESULTS: Sorafenib significantly (P < 0.05) suppressed tumor perfusion (25.1 ± 9.8 to 9.5 ± 6.0 mL/100 mL/min), tumor vascularity (15.6% ± 11.4% to 5.4% ± 2.1%), and PS (8.7 ± 4.5 to 2.7 ± 2.5 mL/100 mL/min) in prostate carcinomas during the treatment course. Immunohistochemistry revealed significantly lower tumor vascularity in the therapy group than in the control group (RECA-1; 181 ± 24 vs. 314 ± 47; P < 0.05). In sorafenib-treated tumors, significantly more apoptotic cells (TUNEL; 7132 ± 3141 vs. 3722 ± 1445; P < 0.05) and significantly less proliferating cells (Ki-67; 9628 ± 1.298 vs. 17,557 ± 1446; P < 0.05) were observed than those in the control group. DCE-CT tumor perfusion correlated significantly (P < 0.05) with tumor cell proliferation (Ki-67; r=0.55). DCE-CT tumor vascularity correlated significantly (P < 0.05) with immunohistochemical tumor cell apoptosis (TUNEL; r=-0.59) and tumor cell proliferation (Ki-67; r=0.68). DCE-CT endothelial PS correlated significantly (P < 0.05) with immunohistochemical tumor cell apoptosis (TUNEL; r=-0.6) and tumor vascularity (RECA-1; r=0.53). While performing corrections for multiple comparisons, we observed a significant correlation only between DCE-CT tumor vascularity (RECA-1) and tumor cell proliferation (Ki-67). CONCLUSION: Sorafenib significantly suppressed tumor perfusion, tumor vascularity, and PS quantified by DCE-CT in experimental prostate carcinomas in rats. These functional CT surrogate markers showed moderate correlations with antiangiogenic, antiproliferative, and proapoptotic effects observed by immunohistochemistry. DCE-CT may be applicable for the quantification of noninvasive imaging biomarkers of therapy response to antiangiogenic therapy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.266
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations42
Published2011
Admission routes1
Has abstractyes

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